nomatteus/environment.yml

## environment.yml
name: example-environment
channels:
  - conda-forge
dependencies:
  - python
  - numpy
  - graphviz
  - pip:
    - nbgitpuller
    - sphinx-gallery
    - pandas
    - matplotlib
    - sklearn
    - pydotplus

## hercodecamp_day2-noanswers.ipynb

      
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## hercodecamp_day2.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "<h3>Building a Cancer Classifier using Random Forest</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "<h4>1- Load The Required Packages</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "import pandas as pd                                              #data manupilation\n",
    "from sklearn.model_selection import train_test_split             #splitting the data to train and test\n",
    "from sklearn import tree                                         #running a decision tree\n",
    "from sklearn.ensemble import RandomForestClassifier              #running a random forest\n",
    "from sklearn import datasets                                     #saved datasets\n",
    "\n",
    "from sklearn import metrics                                      #assessing model performance\n",
    "from sklearn.metrics import classification_report                #assessing model performance\n",
    "from sklearn.metrics import confusion_matrix                     #assessing model performance\n",
    "import matplotlib.pyplot as plt                                  #visualize model performance\n",
    "\n",
    "pd.set_option('display.max_columns', 30)                         #display all columns in your data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "<h4>2- Load The Data</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>radius error</th>\n",
       "      <th>texture error</th>\n",
       "      <th>perimeter error</th>\n",
       "      <th>area error</th>\n",
       "      <th>smoothness error</th>\n",
       "      <th>compactness error</th>\n",
       "      <th>concavity error</th>\n",
       "      <th>concave points error</th>\n",
       "      <th>symmetry error</th>\n",
       "      <th>fractal dimension error</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>1.0950</td>\n",
       "      <td>0.9053</td>\n",
       "      <td>8.589</td>\n",
       "      <td>153.40</td>\n",
       "      <td>0.006399</td>\n",
       "      <td>0.04904</td>\n",
       "      <td>0.05373</td>\n",
       "      <td>0.01587</td>\n",
       "      <td>0.03003</td>\n",
       "      <td>0.006193</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>0.5435</td>\n",
       "      <td>0.7339</td>\n",
       "      <td>3.398</td>\n",
       "      <td>74.08</td>\n",
       "      <td>0.005225</td>\n",
       "      <td>0.01308</td>\n",
       "      <td>0.01860</td>\n",
       "      <td>0.01340</td>\n",
       "      <td>0.01389</td>\n",
       "      <td>0.003532</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>0.7456</td>\n",
       "      <td>0.7869</td>\n",
       "      <td>4.585</td>\n",
       "      <td>94.03</td>\n",
       "      <td>0.006150</td>\n",
       "      <td>0.04006</td>\n",
       "      <td>0.03832</td>\n",
       "      <td>0.02058</td>\n",
       "      <td>0.02250</td>\n",
       "      <td>0.004571</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>0.4956</td>\n",
       "      <td>1.1560</td>\n",
       "      <td>3.445</td>\n",
       "      <td>27.23</td>\n",
       "      <td>0.009110</td>\n",
       "      <td>0.07458</td>\n",
       "      <td>0.05661</td>\n",
       "      <td>0.01867</td>\n",
       "      <td>0.05963</td>\n",
       "      <td>0.009208</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>0.7572</td>\n",
       "      <td>0.7813</td>\n",
       "      <td>5.438</td>\n",
       "      <td>94.44</td>\n",
       "      <td>0.011490</td>\n",
       "      <td>0.02461</td>\n",
       "      <td>0.05688</td>\n",
       "      <td>0.01885</td>\n",
       "      <td>0.01756</td>\n",
       "      <td>0.005115</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  mean radius mean texture mean perimeter mean area mean smoothness  \\\n",
       "0       17.99        10.38         122.80    1001.0         0.11840   \n",
       "1       20.57        17.77         132.90    1326.0         0.08474   \n",
       "2       19.69        21.25         130.00    1203.0         0.10960   \n",
       "3       11.42        20.38          77.58     386.1         0.14250   \n",
       "4       20.29        14.34         135.10    1297.0         0.10030   \n",
       "\n",
       "  mean compactness mean concavity mean concave points mean symmetry  \\\n",
       "0          0.27760         0.3001             0.14710        0.2419   \n",
       "1          0.07864         0.0869             0.07017        0.1812   \n",
       "2          0.15990         0.1974             0.12790        0.2069   \n",
       "3          0.28390         0.2414             0.10520        0.2597   \n",
       "4          0.13280         0.1980             0.10430        0.1809   \n",
       "\n",
       "  mean fractal dimension radius error texture error perimeter error  \\\n",
       "0                0.07871       1.0950        0.9053           8.589   \n",
       "1                0.05667       0.5435        0.7339           3.398   \n",
       "2                0.05999       0.7456        0.7869           4.585   \n",
       "3                0.09744       0.4956        1.1560           3.445   \n",
       "4                0.05883       0.7572        0.7813           5.438   \n",
       "\n",
       "  area error smoothness error compactness error concavity error  \\\n",
       "0     153.40         0.006399           0.04904         0.05373   \n",
       "1      74.08         0.005225           0.01308         0.01860   \n",
       "2      94.03         0.006150           0.04006         0.03832   \n",
       "3      27.23         0.009110           0.07458         0.05661   \n",
       "4      94.44         0.011490           0.02461         0.05688   \n",
       "\n",
       "  concave points error symmetry error fractal dimension error worst radius  \\\n",
       "0              0.01587        0.03003                0.006193        25.38   \n",
       "1              0.01340        0.01389                0.003532        24.99   \n",
       "2              0.02058        0.02250                0.004571        23.57   \n",
       "3              0.01867        0.05963                0.009208        14.91   \n",
       "4              0.01885        0.01756                0.005115        22.54   \n",
       "\n",
       "  worst texture worst perimeter worst area worst smoothness worst compactness  \\\n",
       "0         17.33          184.60     2019.0           0.1622            0.6656   \n",
       "1         23.41          158.80     1956.0           0.1238            0.1866   \n",
       "2         25.53          152.50     1709.0           0.1444            0.4245   \n",
       "3         26.50           98.87      567.7           0.2098            0.8663   \n",
       "4         16.67          152.20     1575.0           0.1374            0.2050   \n",
       "\n",
       "  worst concavity worst concave points worst symmetry worst fractal dimension  \n",
       "0          0.7119               0.2654         0.4601                 0.11890  \n",
       "1          0.2416               0.1860         0.2750                 0.08902  \n",
       "2          0.4504               0.2430         0.3613                 0.08758  \n",
       "3          0.6869               0.2575         0.6638                 0.17300  \n",
       "4          0.4000               0.1625         0.2364                 0.07678  "
      ]
     },
     "execution_count": 4,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer = datasets.load_breast_cancer()\n",
    "X=pd.DataFrame(cancer.data,columns=[cancer.feature_names])      #define your features\n",
    "Y=pd.Series(cancer.target)                                      #define the target variable\n",
    "X.head()                                                        #view the first few rows from your features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-581203140a6e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#print the dimensions of the dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'X' is not defined"
     ]
    }
   ],
   "source": [
    "#print the dimensions of the dataset\n",
    "print(X.shape)\n",
    "print(Y.value_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([(            'mean radius',),\n",
       "            (           'mean texture',),\n",
       "            (         'mean perimeter',),\n",
       "            (              'mean area',),\n",
       "            (        'mean smoothness',),\n",
       "            (       'mean compactness',),\n",
       "            (         'mean concavity',),\n",
       "            (    'mean concave points',),\n",
       "            (          'mean symmetry',),\n",
       "            ( 'mean fractal dimension',),\n",
       "            (           'radius error',),\n",
       "            (          'texture error',),\n",
       "            (        'perimeter error',),\n",
       "            (             'area error',),\n",
       "            (       'smoothness error',),\n",
       "            (      'compactness error',),\n",
       "            (        'concavity error',),\n",
       "            (   'concave points error',),\n",
       "            (         'symmetry error',),\n",
       "            ('fractal dimension error',),\n",
       "            (           'worst radius',),\n",
       "            (          'worst texture',),\n",
       "            (        'worst perimeter',),\n",
       "            (             'worst area',),\n",
       "            (       'worst smoothness',),\n",
       "            (      'worst compactness',),\n",
       "            (        'worst concavity',),\n",
       "            (   'worst concave points',),\n",
       "            (         'worst symmetry',),\n",
       "            ('worst fractal dimension',)],\n",
       "           )"
      ]
     },
     "execution_count": 6,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's look at column names\n",
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
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       "      <th>radius error</th>\n",
       "      <th>texture error</th>\n",
       "      <th>perimeter error</th>\n",
       "      <th>area error</th>\n",
       "      <th>smoothness error</th>\n",
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       "      <th>concavity error</th>\n",
       "      <th>concave points error</th>\n",
       "      <th>symmetry error</th>\n",
       "      <th>fractal dimension error</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
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       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>14.127292</td>\n",
       "      <td>19.289649</td>\n",
       "      <td>91.969033</td>\n",
       "      <td>654.889104</td>\n",
       "      <td>0.096360</td>\n",
       "      <td>0.104341</td>\n",
       "      <td>0.088799</td>\n",
       "      <td>0.048919</td>\n",
       "      <td>0.181162</td>\n",
       "      <td>0.062798</td>\n",
       "      <td>0.405172</td>\n",
       "      <td>1.216853</td>\n",
       "      <td>2.866059</td>\n",
       "      <td>40.337079</td>\n",
       "      <td>0.007041</td>\n",
       "      <td>0.025478</td>\n",
       "      <td>0.031894</td>\n",
       "      <td>0.011796</td>\n",
       "      <td>0.020542</td>\n",
       "      <td>0.003795</td>\n",
       "      <td>16.269190</td>\n",
       "      <td>25.677223</td>\n",
       "      <td>107.261213</td>\n",
       "      <td>880.583128</td>\n",
       "      <td>0.132369</td>\n",
       "      <td>0.254265</td>\n",
       "      <td>0.272188</td>\n",
       "      <td>0.114606</td>\n",
       "      <td>0.290076</td>\n",
       "      <td>0.083946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>3.524049</td>\n",
       "      <td>4.301036</td>\n",
       "      <td>24.298981</td>\n",
       "      <td>351.914129</td>\n",
       "      <td>0.014064</td>\n",
       "      <td>0.052813</td>\n",
       "      <td>0.079720</td>\n",
       "      <td>0.038803</td>\n",
       "      <td>0.027414</td>\n",
       "      <td>0.007060</td>\n",
       "      <td>0.277313</td>\n",
       "      <td>0.551648</td>\n",
       "      <td>2.021855</td>\n",
       "      <td>45.491006</td>\n",
       "      <td>0.003003</td>\n",
       "      <td>0.017908</td>\n",
       "      <td>0.030186</td>\n",
       "      <td>0.006170</td>\n",
       "      <td>0.008266</td>\n",
       "      <td>0.002646</td>\n",
       "      <td>4.833242</td>\n",
       "      <td>6.146258</td>\n",
       "      <td>33.602542</td>\n",
       "      <td>569.356993</td>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>0.208624</td>\n",
       "      <td>0.065732</td>\n",
       "      <td>0.061867</td>\n",
       "      <td>0.018061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>6.981000</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>43.790000</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>0.052630</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>0.049960</td>\n",
       "      <td>0.111500</td>\n",
       "      <td>0.360200</td>\n",
       "      <td>0.757000</td>\n",
       "      <td>6.802000</td>\n",
       "      <td>0.001713</td>\n",
       "      <td>0.002252</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007882</td>\n",
       "      <td>0.000895</td>\n",
       "      <td>7.930000</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>50.410000</td>\n",
       "      <td>185.200000</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.055040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>16.170000</td>\n",
       "      <td>75.170000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>0.086370</td>\n",
       "      <td>0.064920</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.020310</td>\n",
       "      <td>0.161900</td>\n",
       "      <td>0.057700</td>\n",
       "      <td>0.232400</td>\n",
       "      <td>0.833900</td>\n",
       "      <td>1.606000</td>\n",
       "      <td>17.850000</td>\n",
       "      <td>0.005169</td>\n",
       "      <td>0.013080</td>\n",
       "      <td>0.015090</td>\n",
       "      <td>0.007638</td>\n",
       "      <td>0.015160</td>\n",
       "      <td>0.002248</td>\n",
       "      <td>13.010000</td>\n",
       "      <td>21.080000</td>\n",
       "      <td>84.110000</td>\n",
       "      <td>515.300000</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>0.147200</td>\n",
       "      <td>0.114500</td>\n",
       "      <td>0.064930</td>\n",
       "      <td>0.250400</td>\n",
       "      <td>0.071460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>13.370000</td>\n",
       "      <td>18.840000</td>\n",
       "      <td>86.240000</td>\n",
       "      <td>551.100000</td>\n",
       "      <td>0.095870</td>\n",
       "      <td>0.092630</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.033500</td>\n",
       "      <td>0.179200</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.324200</td>\n",
       "      <td>1.108000</td>\n",
       "      <td>2.287000</td>\n",
       "      <td>24.530000</td>\n",
       "      <td>0.006380</td>\n",
       "      <td>0.020450</td>\n",
       "      <td>0.025890</td>\n",
       "      <td>0.010930</td>\n",
       "      <td>0.018730</td>\n",
       "      <td>0.003187</td>\n",
       "      <td>14.970000</td>\n",
       "      <td>25.410000</td>\n",
       "      <td>97.660000</td>\n",
       "      <td>686.500000</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.211900</td>\n",
       "      <td>0.226700</td>\n",
       "      <td>0.099930</td>\n",
       "      <td>0.282200</td>\n",
       "      <td>0.080040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>15.780000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>104.100000</td>\n",
       "      <td>782.700000</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.130400</td>\n",
       "      <td>0.130700</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.195700</td>\n",
       "      <td>0.066120</td>\n",
       "      <td>0.478900</td>\n",
       "      <td>1.474000</td>\n",
       "      <td>3.357000</td>\n",
       "      <td>45.190000</td>\n",
       "      <td>0.008146</td>\n",
       "      <td>0.032450</td>\n",
       "      <td>0.042050</td>\n",
       "      <td>0.014710</td>\n",
       "      <td>0.023480</td>\n",
       "      <td>0.004558</td>\n",
       "      <td>18.790000</td>\n",
       "      <td>29.720000</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.161400</td>\n",
       "      <td>0.317900</td>\n",
       "      <td>0.092080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>28.110000</td>\n",
       "      <td>39.280000</td>\n",
       "      <td>188.500000</td>\n",
       "      <td>2501.000000</td>\n",
       "      <td>0.163400</td>\n",
       "      <td>0.345400</td>\n",
       "      <td>0.426800</td>\n",
       "      <td>0.201200</td>\n",
       "      <td>0.304000</td>\n",
       "      <td>0.097440</td>\n",
       "      <td>2.873000</td>\n",
       "      <td>4.885000</td>\n",
       "      <td>21.980000</td>\n",
       "      <td>542.200000</td>\n",
       "      <td>0.031130</td>\n",
       "      <td>0.135400</td>\n",
       "      <td>0.396000</td>\n",
       "      <td>0.052790</td>\n",
       "      <td>0.078950</td>\n",
       "      <td>0.029840</td>\n",
       "      <td>36.040000</td>\n",
       "      <td>49.540000</td>\n",
       "      <td>251.200000</td>\n",
       "      <td>4254.000000</td>\n",
       "      <td>0.222600</td>\n",
       "      <td>1.058000</td>\n",
       "      <td>1.252000</td>\n",
       "      <td>0.291000</td>\n",
       "      <td>0.663800</td>\n",
       "      <td>0.207500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      mean radius mean texture mean perimeter    mean area mean smoothness  \\\n",
       "count  569.000000   569.000000     569.000000   569.000000      569.000000   \n",
       "mean    14.127292    19.289649      91.969033   654.889104        0.096360   \n",
       "std      3.524049     4.301036      24.298981   351.914129        0.014064   \n",
       "min      6.981000     9.710000      43.790000   143.500000        0.052630   \n",
       "25%     11.700000    16.170000      75.170000   420.300000        0.086370   \n",
       "50%     13.370000    18.840000      86.240000   551.100000        0.095870   \n",
       "75%     15.780000    21.800000     104.100000   782.700000        0.105300   \n",
       "max     28.110000    39.280000     188.500000  2501.000000        0.163400   \n",
       "\n",
       "      mean compactness mean concavity mean concave points mean symmetry  \\\n",
       "count       569.000000     569.000000          569.000000    569.000000   \n",
       "mean          0.104341       0.088799            0.048919      0.181162   \n",
       "std           0.052813       0.079720            0.038803      0.027414   \n",
       "min           0.019380       0.000000            0.000000      0.106000   \n",
       "25%           0.064920       0.029560            0.020310      0.161900   \n",
       "50%           0.092630       0.061540            0.033500      0.179200   \n",
       "75%           0.130400       0.130700            0.074000      0.195700   \n",
       "max           0.345400       0.426800            0.201200      0.304000   \n",
       "\n",
       "      mean fractal dimension radius error texture error perimeter error  \\\n",
       "count             569.000000   569.000000    569.000000      569.000000   \n",
       "mean                0.062798     0.405172      1.216853        2.866059   \n",
       "std                 0.007060     0.277313      0.551648        2.021855   \n",
       "min                 0.049960     0.111500      0.360200        0.757000   \n",
       "25%                 0.057700     0.232400      0.833900        1.606000   \n",
       "50%                 0.061540     0.324200      1.108000        2.287000   \n",
       "75%                 0.066120     0.478900      1.474000        3.357000   \n",
       "max                 0.097440     2.873000      4.885000       21.980000   \n",
       "\n",
       "       area error smoothness error compactness error concavity error  \\\n",
       "count  569.000000       569.000000        569.000000      569.000000   \n",
       "mean    40.337079         0.007041          0.025478        0.031894   \n",
       "std     45.491006         0.003003          0.017908        0.030186   \n",
       "min      6.802000         0.001713          0.002252        0.000000   \n",
       "25%     17.850000         0.005169          0.013080        0.015090   \n",
       "50%     24.530000         0.006380          0.020450        0.025890   \n",
       "75%     45.190000         0.008146          0.032450        0.042050   \n",
       "max    542.200000         0.031130          0.135400        0.396000   \n",
       "\n",
       "      concave points error symmetry error fractal dimension error  \\\n",
       "count           569.000000     569.000000              569.000000   \n",
       "mean              0.011796       0.020542                0.003795   \n",
       "std               0.006170       0.008266                0.002646   \n",
       "min               0.000000       0.007882                0.000895   \n",
       "25%               0.007638       0.015160                0.002248   \n",
       "50%               0.010930       0.018730                0.003187   \n",
       "75%               0.014710       0.023480                0.004558   \n",
       "max               0.052790       0.078950                0.029840   \n",
       "\n",
       "      worst radius worst texture worst perimeter   worst area  \\\n",
       "count   569.000000    569.000000      569.000000   569.000000   \n",
       "mean     16.269190     25.677223      107.261213   880.583128   \n",
       "std       4.833242      6.146258       33.602542   569.356993   \n",
       "min       7.930000     12.020000       50.410000   185.200000   \n",
       "25%      13.010000     21.080000       84.110000   515.300000   \n",
       "50%      14.970000     25.410000       97.660000   686.500000   \n",
       "75%      18.790000     29.720000      125.400000  1084.000000   \n",
       "max      36.040000     49.540000      251.200000  4254.000000   \n",
       "\n",
       "      worst smoothness worst compactness worst concavity worst concave points  \\\n",
       "count       569.000000        569.000000      569.000000           569.000000   \n",
       "mean          0.132369          0.254265        0.272188             0.114606   \n",
       "std           0.022832          0.157336        0.208624             0.065732   \n",
       "min           0.071170          0.027290        0.000000             0.000000   \n",
       "25%           0.116600          0.147200        0.114500             0.064930   \n",
       "50%           0.131300          0.211900        0.226700             0.099930   \n",
       "75%           0.146000          0.339100        0.382900             0.161400   \n",
       "max           0.222600          1.058000        1.252000             0.291000   \n",
       "\n",
       "      worst symmetry worst fractal dimension  \n",
       "count     569.000000              569.000000  \n",
       "mean        0.290076                0.083946  \n",
       "std         0.061867                0.018061  \n",
       "min         0.156500                0.055040  \n",
       "25%         0.250400                0.071460  \n",
       "50%         0.282200                0.080040  \n",
       "75%         0.317900                0.092080  \n",
       "max         0.663800                0.207500  "
      ]
     },
     "execution_count": 7,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's summarize the data\n",
    "X.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "<h4>3- Split to Train and Test</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(398, 30)\n",
      "(171, 30)\n",
      "(398,)\n",
      "(171,)\n"
     ]
    }
   ],
   "source": [
    "#split the data to 70% train and 30% test\n",
    "x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size=0.3,random_state=42)\n",
    "\n",
    "print(x_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "<h4>4- Train your model: Random Forest</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9849246231155779"
      ]
     },
     "execution_count": 9,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_model = RandomForestClassifier(max_depth=3,n_estimators=15)        #define the model\n",
    "rf_model.fit(x_train, y_train)                                        #fit the model (train)\n",
    "rf_model.score(x_train,y_train)                                       #predict on new observations\n",
    "\n",
    "#what is the accuracy of this model?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Let's visualize this tree! (https://medium.com/@rnbrown/creating-and-visualizing-decision-trees-with-python-f8e8fa394176)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/externals/six.py:31: DeprecationWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
      "  \"(https://pypi.org/project/six/).\", DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#select which tree do you want to visualize\n",
    "selected_tree=2\n",
    "\n",
    "from sklearn.externals.six import StringIO\n",
    "from IPython.display import Image\n",
    "from sklearn.tree import export_graphviz\n",
    "import pydotplus\n",
    "dot_data2 = StringIO()\n",
    "export_graphviz(rf_model.estimators_[selected_tree],\n",
    "                out_file=dot_data2,\n",
    "                filled=True,\n",
    "                precision=2,\n",
    "                feature_names=x_train.columns,\n",
    "                rounded=True)\n",
    "graph = pydotplus.graph_from_dot_data(dot_data2.getvalue())\n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "<h4>5- Predict!</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>radius error</th>\n",
       "      <th>texture error</th>\n",
       "      <th>perimeter error</th>\n",
       "      <th>area error</th>\n",
       "      <th>smoothness error</th>\n",
       "      <th>compactness error</th>\n",
       "      <th>concavity error</th>\n",
       "      <th>concave points error</th>\n",
       "      <th>symmetry error</th>\n",
       "      <th>fractal dimension error</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>204</td>\n",
       "      <td>12.47</td>\n",
       "      <td>18.6</td>\n",
       "      <td>81.09</td>\n",
       "      <td>481.9</td>\n",
       "      <td>0.09965</td>\n",
       "      <td>0.1058</td>\n",
       "      <td>0.08005</td>\n",
       "      <td>0.03821</td>\n",
       "      <td>0.1925</td>\n",
       "      <td>0.06373</td>\n",
       "      <td>0.3961</td>\n",
       "      <td>1.044</td>\n",
       "      <td>2.497</td>\n",
       "      <td>30.29</td>\n",
       "      <td>0.006953</td>\n",
       "      <td>0.01911</td>\n",
       "      <td>0.02701</td>\n",
       "      <td>0.01037</td>\n",
       "      <td>0.01782</td>\n",
       "      <td>0.003586</td>\n",
       "      <td>14.97</td>\n",
       "      <td>24.64</td>\n",
       "      <td>96.05</td>\n",
       "      <td>677.9</td>\n",
       "      <td>0.1426</td>\n",
       "      <td>0.2378</td>\n",
       "      <td>0.2671</td>\n",
       "      <td>0.1015</td>\n",
       "      <td>0.3014</td>\n",
       "      <td>0.0875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean radius mean texture mean perimeter mean area mean smoothness  \\\n",
       "204       12.47         18.6          81.09     481.9         0.09965   \n",
       "\n",
       "    mean compactness mean concavity mean concave points mean symmetry  \\\n",
       "204           0.1058        0.08005             0.03821        0.1925   \n",
       "\n",
       "    mean fractal dimension radius error texture error perimeter error  \\\n",
       "204                0.06373       0.3961         1.044           2.497   \n",
       "\n",
       "    area error smoothness error compactness error concavity error  \\\n",
       "204      30.29         0.006953           0.01911         0.02701   \n",
       "\n",
       "    concave points error symmetry error fractal dimension error worst radius  \\\n",
       "204              0.01037        0.01782                0.003586        14.97   \n",
       "\n",
       "    worst texture worst perimeter worst area worst smoothness  \\\n",
       "204         24.64           96.05      677.9           0.1426   \n",
       "\n",
       "    worst compactness worst concavity worst concave points worst symmetry  \\\n",
       "204            0.2378          0.2671               0.1015         0.3014   \n",
       "\n",
       "    worst fractal dimension  \n",
       "204                  0.0875  "
      ]
     },
     "execution_count": 11,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's pull information from one patient from the test set\n",
    "patient1_test=(x_test.iloc[0:1,:])\n",
    "patient1_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 12,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#what would our model predict? Malignant or Benign?\n",
    "rf_model.predict(patient1_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.09455427, 0.90544573]])"
      ]
     },
     "execution_count": 13,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#can we predict the probability of a patient being malignant or benign?\n",
    "rf_model.predict_proba(patient1_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1,\n",
       "       0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n",
       "       0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1,\n",
       "       0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0,\n",
       "       1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1,\n",
       "       1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1])"
      ]
     },
     "execution_count": 14,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Can we predict multiple patients at once?\n",
    "rf_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.09455427, 0.90544573],\n",
       "       [0.98808391, 0.01191609],\n",
       "       [0.98808391, 0.01191609],\n",
       "       [0.02520654, 0.97479346],\n",
       "       [0.01285535, 0.98714465],\n",
       "       [0.98557183, 0.01442817],\n",
       "       [0.99001627, 0.00998373],\n",
       "       [0.85784801, 0.14215199],\n",
       "       [0.79946919, 0.20053081],\n",
       "       [0.01329979, 0.98670021]])"
      ]
     },
     "execution_count": 15,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#can we get the probability of each test case being malignant or benign? (display the first 10 lines)\n",
    "rf_model.predict_proba(x_test)[0:10]\n",
    "\n",
    "#do you see how the 0 and 1 were generated in the previous command?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "<h4>6- How well did we predict?</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9649122807017544"
      ]
     },
     "execution_count": 16,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#what is the accuracy of the model on the test set?\n",
    "rf_model.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predicted benign</th>\n",
       "      <th>predicted malignant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>benign</td>\n",
       "      <td>59</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>malignant</td>\n",
       "      <td>2</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           predicted benign  predicted malignant\n",
       "benign                   59                    4\n",
       "malignant                 2                  106"
      ]
     },
     "execution_count": 17,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's generate a confusion matrix!\n",
    "pd.DataFrame(confusion_matrix(y_test,rf_model.predict(x_test)),index=['benign','malignant'],columns=['predicted benign','predicted malignant'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "<h4>7- Identifying the important questions!</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "#let's create a data frame that contains information about how important each question is in generating the correct prediction!\n",
    "feature_importances = pd.DataFrame(rf_model.feature_importances_,\n",
    "                                   index = x_train.columns,\n",
    "                                    columns=['importance']).sort_values('importance',                                                                 ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>worst concave points</td>\n",
       "      <td>0.191349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>worst radius</td>\n",
       "      <td>0.144533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean concave points</td>\n",
       "      <td>0.111866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>worst concavity</td>\n",
       "      <td>0.107259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean radius</td>\n",
       "      <td>0.092839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean area</td>\n",
       "      <td>0.071659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>worst area</td>\n",
       "      <td>0.065903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>area error</td>\n",
       "      <td>0.041340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>worst perimeter</td>\n",
       "      <td>0.026521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean texture</td>\n",
       "      <td>0.023976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>worst compactness</td>\n",
       "      <td>0.022208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>concavity error</td>\n",
       "      <td>0.019147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>worst symmetry</td>\n",
       "      <td>0.018041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>worst smoothness</td>\n",
       "      <td>0.013202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean concavity</td>\n",
       "      <td>0.012525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>worst texture</td>\n",
       "      <td>0.011488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>perimeter error</td>\n",
       "      <td>0.004753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean compactness</td>\n",
       "      <td>0.004630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>fractal dimension error</td>\n",
       "      <td>0.003869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>radius error</td>\n",
       "      <td>0.003524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>concave points error</td>\n",
       "      <td>0.003100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean perimeter</td>\n",
       "      <td>0.002389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean smoothness</td>\n",
       "      <td>0.001754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>symmetry error</td>\n",
       "      <td>0.001535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>compactness error</td>\n",
       "      <td>0.000589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>smoothness error</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>texture error</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean fractal dimension</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean symmetry</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>worst fractal dimension</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         importance\n",
       "worst concave points       0.191349\n",
       "worst radius               0.144533\n",
       "mean concave points        0.111866\n",
       "worst concavity            0.107259\n",
       "mean radius                0.092839\n",
       "mean area                  0.071659\n",
       "worst area                 0.065903\n",
       "area error                 0.041340\n",
       "worst perimeter            0.026521\n",
       "mean texture               0.023976\n",
       "worst compactness          0.022208\n",
       "concavity error            0.019147\n",
       "worst symmetry             0.018041\n",
       "worst smoothness           0.013202\n",
       "mean concavity             0.012525\n",
       "worst texture              0.011488\n",
       "perimeter error            0.004753\n",
       "mean compactness           0.004630\n",
       "fractal dimension error    0.003869\n",
       "radius error               0.003524\n",
       "concave points error       0.003100\n",
       "mean perimeter             0.002389\n",
       "mean smoothness            0.001754\n",
       "symmetry error             0.001535\n",
       "compactness error          0.000589\n",
       "smoothness error           0.000000\n",
       "texture error              0.000000\n",
       "mean fractal dimension     0.000000\n",
       "mean symmetry              0.000000\n",
       "worst fractal dimension    0.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display the dataframe. Which questions do you think are important?\n",
    "feature_importances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "<h4>8- Let's build another model with less features!</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>worst concavity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>184.60</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>25.38</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.7119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>158.80</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>24.99</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.2416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>152.50</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>23.57</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.4504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>98.87</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>14.91</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.6869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>152.20</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>22.54</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.4000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  worst perimeter worst concave points worst radius mean concave points  \\\n",
       "0          184.60               0.2654        25.38             0.14710   \n",
       "1          158.80               0.1860        24.99             0.07017   \n",
       "2          152.50               0.2430        23.57             0.12790   \n",
       "3           98.87               0.2575        14.91             0.10520   \n",
       "4          152.20               0.1625        22.54             0.10430   \n",
       "\n",
       "  worst concavity  \n",
       "0          0.7119  \n",
       "1          0.2416  \n",
       "2          0.4504  \n",
       "3          0.6869  \n",
       "4          0.4000  "
      ]
     },
     "execution_count": 20,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#subset the questions we are interested in\n",
    "X_reduced=X[['worst perimeter','worst concave points','worst radius','mean concave points','worst concavity']]   #define your features\n",
    "Y=pd.Series(cancer.target)                                                                                       #define the target\n",
    "X_reduced.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "#split into train and test\n",
    "x_train,x_test,y_train,y_test = train_test_split(X_reduced,Y,test_size=0.3,random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>worst concavity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>204</td>\n",
       "      <td>96.05</td>\n",
       "      <td>0.10150</td>\n",
       "      <td>14.97</td>\n",
       "      <td>0.03821</td>\n",
       "      <td>0.2671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>165.90</td>\n",
       "      <td>0.17890</td>\n",
       "      <td>24.86</td>\n",
       "      <td>0.07951</td>\n",
       "      <td>0.2687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>131</td>\n",
       "      <td>124.90</td>\n",
       "      <td>0.15140</td>\n",
       "      <td>19.26</td>\n",
       "      <td>0.08087</td>\n",
       "      <td>0.3791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>431</td>\n",
       "      <td>89.61</td>\n",
       "      <td>0.07370</td>\n",
       "      <td>12.88</td>\n",
       "      <td>0.02799</td>\n",
       "      <td>0.2403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>540</td>\n",
       "      <td>78.78</td>\n",
       "      <td>0.06918</td>\n",
       "      <td>12.26</td>\n",
       "      <td>0.02594</td>\n",
       "      <td>0.1797</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    worst perimeter worst concave points worst radius mean concave points  \\\n",
       "204           96.05              0.10150        14.97             0.03821   \n",
       "70           165.90              0.17890        24.86             0.07951   \n",
       "131          124.90              0.15140        19.26             0.08087   \n",
       "431           89.61              0.07370        12.88             0.02799   \n",
       "540           78.78              0.06918        12.26             0.02594   \n",
       "\n",
       "    worst concavity  \n",
       "204          0.2671  \n",
       "70           0.2687  \n",
       "131          0.3791  \n",
       "431          0.2403  \n",
       "540          0.1797  "
      ]
     },
     "execution_count": 24,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.head()\n",
    "# y_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9623115577889447"
      ]
     },
     "execution_count": 20,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#train a new model!\n",
    "rf_model = RandomForestClassifier(max_depth=3,n_estimators=15)        #define the model\n",
    "rf_model.fit(x_train, y_train)                                        #fit the model (train)\n",
    "rf_model.score(x_train,y_train)                                       #predict on new observations\n",
    "\n",
    "#what is the accuracy of this model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['cancer_classifier.pkl']"
      ]
     },
     "execution_count": 21,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#save the model!\n",
    "from sklearn.externals import joblib\n",
    "\n",
    "joblib.dump(rf_model, \"cancer_classifier.pkl\")    #save the whole model into a file to be used later\n",
    "\n",
    "#to load the model next time we just need to do:\n",
    "#classifer = joblib.load(\"model.pkl\")\n",
    "#classifer.predict(newobs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "<center><h3>Congratulations! You have built your first classifier!</h3></center>\n",
    "<center><h5>www.thecodinghive.com</h5></center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
   ]
  }
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	name: example-environment
	channels:
	- conda-forge
	dependencies:
	- python
	- numpy
	- graphviz
	- pip:
	- nbgitpuller
	- sphinx-gallery
	- pandas
	- matplotlib
	- sklearn
	- pydotplus